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SMT iconnect007 Part 1/3: Filling Traceability Gaps With AI

July 2023

Filling critical traceability gaps with ai
Traceability means being able to track the
origin of any given electrical component
throughout the supply chain. For OEMs, this is
no longer optional or “nice to have.” Yet industrial traceability capacities are sorely lacking
throughout industries.
Today, the most widespread standard for
traceability is “batch traceability,” which aside
from tracking the production lot, serial number, and exact board placement for components, fails to analyze the individual components themselves, thus jeopardizing the quality of the goods they compose.
Faulty components are unavoidable. For
components that make it into circulation and
harbor some kind of defect, the result can easily cascade into sweeping recalls that waste
time, money, and resources.

Instead, electronics manufacturers need to be able to facilitate recalls much more surgically. But that can only be achieved through exploratory traceability: new processes that employ precise, detailed, and exacting visual
identification of every electronic component placed on a PCBA. Original equipment manufacturers have an opportunity to improve traceability with AI tools and big data, closing the information gaps that plague tech products across the supply chain.

Where Lies the Fault?
Because eight out of 10 failures are attributed
to faulty components rather than faulty workmanship, most manufacturers view traceability as essential. After all, it allows them to operate in markets that demand a detailed trail for
every part on every board. Consider military defense systems, automotive software, and medical technologies, where consequences of a malfunction can be dire. Unfortunately, supply chain disruptions,
especially in the wake of the pandemic, muddied the waters. Average manufacturing lead times increased from three and a half months to nearly a year, forcing OEMs to circumvent their standard suppliers and source components from alternative suppliers, often with improper storage standards and subpar materials, as well as mixed lots and ambiguous traceability information.

Although the negative impacts of supply chain slowdowns on electronic components
have diminished, assuming there are zero defects across thousands upon thousands of
components would be wrong-minded. Given that defective and counterfeit components
remain, manufacturers should raise their traceability standards to boost quality, output efficiency, and financial clarity, all of which help strengthen the bottom line.Leveraging AI to do this offers the path of least resistance.

Filling critical traceability gaps with ai

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